DETECTING PEDIATRIC MALNUTRITION: TOOLS FOR SCREENING & CRITERIA FOR DIAGNOSING

THE ABBOTT NUTRITION HEALTH INSTITUTE

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DIETITIANCONNECTION.COM

MAREE FERGUSONPATRICIA BECKER

MELINDA WHITE

DISCLOSURES

• The content of this program has met the continuing education criteria of being evidence-based, fair and balanced, and non-promotional

• This educational event is supported by Abbott Nutrition Health Institute, Abbott Nutrition and Dietitian Connection

• Maree Ferguson is the Founder of Dietitian Connection

• Patricia Becker is a self-study program reviewer for ANHI

• Melinda White receives research support from Children’s Hospital Foundation and Nestle Australia

OBJECTIVES

• Review the goals, purpose and definition of malnutrition indicators as defined by ASPEN and AND

• Utilize the malnutrition indicators and identify their implications on the diagnosis of pediatric malnutrition

• Define and understand how to implement pediatric malnutrition screening and rescreening tools

PATRICIA BECKERMS, RDN, CSP, CNSC, FAND

PEDIATRIC NUTRITION SPECIALIST

ACADEMY/A.S.P.E.N. CONSENSUS STATEMENT: PURPOSE

• ID characteristics/indicators that can be used to diagnose and document pediatric undernutrition in children 1 month – 18 years old– Macronutrient focus

– Preterm infants and neonates not addressed

• Use in all settings

DEFINITION OF UNDERNUTRITION

• An imbalance between nutrient requirement and intake, resulting in cumulative deficits of energy, protein or micronutrients that negatively affects growth, development and other relevant outcomes

• Causes related to:– Diseases/conditions; acute or chronic

– Adverse environmental/social/behavioral factors

ATTRIBUTES OF RECOMMENDED INDICATORS

• Basic parameters– Few in number

– Easily obtained in multiple settings

• Support a diagnosis of undernutrition

• Characterize severity

• Reflect changes in nutritional status– Rather than degree of inflammation/disease acuity

• Evidence informed/consensus derived

• May change over time as evidence of validity accrues

INTENDED POPULATION

1. Term infants one month of age or greater

2. Infants with a corrected gestational age to term (37 weeks or greater) day of life 30 or greater

3. Children up to and including 18 years of age

RECOMMENDED INDICATORS• Height-for-age z-score when indicated

• Weight gain velocity

• Adequacy of food/nutrient intake & utilization

• Weight-for-length or BMI-for-age z-score

• Weight loss percentage of usual body

• Mid-Upper Arm Circumference (MUAC)

• Considered indictors:– Handgrip Strength (ages 6+)

– Tanner Stage

• Should be tracked in all pre-teens/adolescents

• Utility as a nutritional marker limited by the significant variability in genetic determinants for onset of puberty

PRIMARY INDICATORS: SINGLE DATA POINT AVAILABLE

Mild malnutrition Moderate malnutrition Severe malnutrition

Weight-for-height z-score

-1 to -1.9 z-score -2 to -2.9 z-score -3 or greater z-score

BMI-for-age z-score -1 to – 1.9 z-score -2 to -2.9 z-score -3 or greater z-score

Length / height z-score No data No data -3 z-score

Mid-upper arm circumference

Less than – 1 z-score for age / 12.5-13.4cm

Less than -2 z-score for age / < 12.5 cm

Less than – 3 z-score for age / <11.5 cm

PRIMARY INDICATORS: TWO OR MORE DATA POINTS AVAILABLE

Mild malnutrition Moderate malnutrition Severe malnutrition

Weight gain velocity (< 2 years of age)

Less than 75% *of the norm+ for expected weight gain

Less than 50%* of the norm+ for expected weight gain*

Less than 25%* of the norm+ for expected weight gain*

Weight loss(2-20 years of age)

5% usual body weight 7.5% usual body weight 10% usual body weight

Deceleration in weight for length / height Z-score

Decline of 1 z-score Decline of 2 z-score Decline of 3 z-score

Inadequate nutrient intake

51-75% estimated energy / protein need

26-50% estimated energy / protein need

≤ 25% estimated energy / protein need

CHILDREN 0-24 MONTHS

• Weight for length z-score

• Weight gain velocity percentage of expected

• Adequacy of energy – protein intake

• Length for age z-score

• Decline in weigh for length z-score

• MUAC z-score

WEIGHT GAIN VELOCITY ASSESSMENTBoysAge

Grams per day(median)

Grams per mo. (median)

Mild malnutrition ≤75% expected Moderate malnutrition ≤50% expected Severe malnutrition ≤25% expected

0-30 days 35 1025 26 g/d 770 g/mo. 17g/d 513 g/mo. 9 g/d 256 g/mo.

30-60 days 40 1200 30 900 20 600 10 300

2-4 months 27 815 20 611 13.5 407 7 204

4-6 months 16 475 12 360 8 238 4 120

6-9 months 11 330 8 250 6 165 3 83

9-12 months 9 254 6 191 4.5 127 2 64

12-18 months 7 200 5 150 3.5 100 2 50

18-24 months 7 195 5 147 3 98 2 49

Girls Age

Grams per day(median)

Grams per mo. (median)

Mild malnutrition ≤75% expected Moderate malnutrition ≤50% expected Severe malnutrition ≤25% expected

0-30 days 30 880 22 g/d 660 g/mo. 15 g/d 440 g/mo. 7.5 g/d 220 g/mo.

30-60 days 34 1012 25 760 17 506 8.5 253

2-4 months 24 720 18 540 12 360 6 180

4-6 months 15 445 11 334 7.5 223 4 111

6-9 months 10 310 8 250 6 173 3 86

9-12 months 8 240 6 180 4 120 2 60

12-18 months 7 200 5 150 3.5 100 2 50

18-24 months 7 195 5 147 3 98 2 49

USING Z-SCORES: WHY Z-SCORES ARE BETTER

• Allows for description of change for growth below the 3rd%ile

• Allows for calculation of changes in standard deviation

• Only calculation of z-scores allows for a discrete number that could identify improvement in the shorter term…

• For scores that are above +2 or below the -2 z-score line / above the 95th%ile and below the 5th%ile

• Percentiles are intended to be stated as intervals; such as “between the 5th and the 10th” if a child falls between lines, not as a discrete number (7th%ile)

COMPARISON OF PERCENTILES AND Z-SCORES

• A z-score of +3 and -3 is more likely to be abnormal

• Percentile graphs have a narrower range

• 97th%ile corresponds to +2 z-score

• 3rd%ile corresponds to -2 z-score

CHILDREN 2 TO 18 YEARS

• BMI-for-age z-score

• Decline in BMI-for-age z-score

• Weight loss percentage of usual body weight

• Adequacy of energy – protein intake percentage of estimated need

• MUAC z-score

• Height-for-age z-score

WEIGHT LOSS PERCENTAGE OF USUAL BODY WEIGHT

• Nutritionally significant weight loss for adults is time dependent

• Nutritionally significant weight loss for children is not time dependent

• Usual body weight for growing children is a challenge

• Most recent stable weight vs. highest weight

MAKING THE DIAGNOSIS

• Assess all available data

• There may be more than one level of severity / acuity of malnutrition resulting from the data. It is appropriate to select / assign the highest or most severe level of malnutrition based on the criteria, as this will impact the intervention, care and treatment plan for the child

• Example:– If the adequacy of intake suggests moderate malnutrition, but the weight

gain velocity percentage of the norm or the weight-for-length z-score indicate severe a diagnosis of severe malnutrition should be documented

MAKING A DIAGNOSIS

• Nutrition focused physical examination

– Macronutrient assessment vs. micronutrient assessment

• Physical examination of subcutaneous fat loss and muscle wasting

• Physical exam of fluid status

• Assessing functional status

• Physical exam of hair, face, (eyes, nose, mouth) nails, skin

• Use the International Dietetics and Nutrition Terminology

• Problem Etiology Signs/Symptoms (PES) Statement

– Severe pediatric malnutrition (chronic) related to inadequate nutrient intake as evidenced by weight-for-length z-score less than -3 z-score with reduced muscle mass, visible rib and spine, pale conjunctiva

– Moderate malnutrition (acute) related to nutrient loss from high ostomy output as evidenced by weight loss percentage of usual body weight between 7.5 – 9.9%

– Mild malnutrition (chronic) related to IBD as evidenced by decline in BMI-for- age z-score between -1 to -1.99

DOCUMENTING THE DIAGNOSIS

THE ROLE OF THE PEDIATRIC DIETITIAN• Include your nutrition-focused physical findings in your nutrition diagnosis

• Use your nutrition diagnosis to support your nutrition interventions

• Notify your health care team members of your nutrition diagnosis and your nutrition intervention recommendations

• Monitor and evaluate your patient’s response to the intervention and changes to their malnutrition severity / acuity

• Document changes in the medical record

• Notify your care team of changes

• Get to know your medical coders to assist them with needed coding information related to malnutrition billing and coding

• Educate your health care team on the recommended indicators for the identification and documentation of pediatric malnutrition

MELINDA WHITE PHD, GRAD DIP NUTR&DIET, BSC, APD

DIRECTOR, CLINICAL NUTRITION

PEDIATRIC NUTRITION SCREENING TOOLS

• STAMP (Screening Tool for the Assessment for Malnutrition in Paediatrics)1

– Diagnosis with nutritional implications

– Change in nutritional intake

– Comparison of height and weight to percentiles

• STRONGkids (Screening Tool for Risk on Nutritional Status and Growth)2

– Subjective Clinical Assessment (1 point)

– High Risk Disease (2 points) [refer to table]

– Weight loss or poor weight gain? (1 point)

– Nutritional intake and losses (1 point)

• Excessive diarrhoea ≥ 5 per day and/or vomiting (>3 times per day) the last few days?

• Reduced food intake during the last few days before admission

• Pre-existing dietetically advised nutritional intervention?

• Inability to consume adequate intake because of pain?

1. McCarthy H, et al. J Hum Nutr Diet. 2012;25(4):311-3182. Hulst JM, et al. Clin Nutr. 2010;29(1):106-111.

• PYMS (Paediatric Yorkhill Malnutrition Score)1

– BMI compared to cut-off values

– Weight loss

– Reduced intake

• PeDiSMART (Pediatric Digital Scaled Malnutrition Risk Screening Tool)2

– Nutritional status (weight z-score)

– GI symptoms

– Disease impact

• PNST (Paediatric Nutrition Screening Tool)3

– Weight loss

– Poor weight gain

– Reduced nutritional intake

– Physical presentation

1. Gerasimidis K, et al. Br J Nutr. 2010;104(5):751-6.2. Karagiozoglou-Lampoudi T, et al. JPEN. 2015;39(4):418-25.3. White M, et al. JPEN. 2016;40(3):392-8.

PEDIATRIC NUTRITION SCREENING TOOLS

HOW DO YOU CHOOSE?

• Quick: less than 5 minutes, no need to refer to other material

• Simple: no user training needed

• Valid: specific and sensitive

• Easy to implement

• Universal

• Screen Vs Assessment

• Inexpensive

• Non-invasive

• Monitor for nutritional deterioration

IMPLEMENTING SCREENING TOOLS

• Embedded into the nursing admission process

• Referral process

• Training

• Triage of screening referrals

• Time frames to review at risk patients

• Nutrition assessment methodology

• Formation of nutrition care plans and frequency for ongoing review

• Rescreening

• Auditing and monitoring

• Promotion

• Population specific screening tools

• Duty of care

BARRIERS TO SCREENING

• Time– Timeframe for completion

• Complexity– Simple

• Discomfort with subjective nature of screening– Guide for completion

• Lack of organisational support– Incorporation into hospital accreditation

– Embed into routine practice

– Metrics for communication to executive

BARRIERS TO SCREENING

• Lack of RCT to demonstrate influence on clinical outcome– Predicts lengths of stay1

– Increases malnutrition awareness2

– Links to clinical coding, US study coding only identified 1.3% malnutrition3

• Concern with an increase in referrals

1. Galeria-Martinez R, et al. JPGN. 64(3):e65-e70.2. Rub G, et al. JPGN. 62(5):771-5. 3. Abdelhadi R, et al. JPEN. 2016;40(5):623-35.

RCT – Randomized Controlled Trial

NUTRITION SURVEILLANCE DURING HOSPITAL STAY: RESCREENING

• STRONGkids, PYMS, PeDiSMART, STAMP

– No targeted validation for children who stay longer than 7 days

– Contain criteria of diagnosis and anthropometric categorisation against standards which do not measure nutritional deterioration

• The environment is right– Progression of care via digital platforms

creates an opportunity for refinement of the screening process with the introduction of regular ‘rescreening’ as a weekly nursing order or task

– Malnutrition is classed as a Hospital Acquired Complication (HAC)

AIM: RESCREENING

To design and validate a simple, quick and easy nutrition screening tool that can be repeated to detect recent nutritional deterioration in long stay paediatric patients

White S, et al. Clin Nutr ESPEN. 2019;34:55-60.

Clin Nutr ESPEN, 34, 55-60 Dec 2019

A Simple Nutrition Screening Tool to Identify Nutritional Deterioration in Long Stay Paediatric Inpatients: The Paediatric Nutrition Rescreening Tool (PNRT)Melinda S White, Melinda Ziemann, Annabel Doolan, Shang Qian Song, Anne Bernard

METHODS• Prospective longitudinal study in Queensland Children’s Hospital

– 359 beds

• Change in weight over 7 days vs has the child lost weight or had poor weight gain in the last 7 days?

• Change in energy and protein intake over 7 days vs has the child had reduced nutritional intake in the last 7 days?

White S, et al. Clin Nutr ESPEN. 2019;34:55-60.

150 patients approached

N=88 consented

N=61 (Ax + at least 1 Ax

and rescreen 7 days later)

Total 224 data points collected over 1-20 weeks

(median 6 weeks)

27 excluded (d/c before 7 day

follow up assessment)

White S, et al. Clin Nutr ESPEN. 2019;34:55-60.

DESCRIPTIVE STATISTICS

35

44%

56%

Children with one week of data (n = 61):

• Age = range from 0-16, average of 4.5 years old

• BMI z-score average was -0.65 (Min = -3.67, Max = 2.27)

• MUAC z-score average was -0.75 (Min = -3.49, Max = 0.1.84)

Primary Diagnosis

Cardiac Cystic FibrosisGastroenterolgy OncologySurgical Other

White S, et al. Clin Nutr ESPEN. 2019;34:55-60.

Variable Sensitivity Specificity AUC

Any reduction in energy intake (Δ in energy intake) 28.71% 83.61% 0.56 (poor)

Any reduction in protein intake (Δ in protein intake) 28.57% 84.68% 0.57 (poor)

Reduction in ≥ 25% of energy intake 61.9% 82.18% 0.72 (good)

Reduction in ≥ 25% of protein intake 55.56 % 82.65 % 0.69 (moderate)

Reduction in ≥ 50% of energy intake (n=8) 85.71% 80.09% 0.83 (excellent)

Reduction in ≥ 50% of protein intake 80% 80.75% 0.8 (excellent)

Q1. Has the child had reduced nutritional intake in the last 7 days?

AUC = Area under the curve

Q2. Has the child had any weight loss or poor weight gain in the last 7 days?

Variable Sensitivity Specificity AUC Any decrease in weight 71.43% 87.77% 0.8 (excellent)

Weight loss or poor weight gain 42.3% 90.8% 0.66 (moderate)

Any change in BMI z-score 40.91% 89.58 % 0.65 (moderate)

5% reduction in BMI z-score 39.34% 87.92% 0.64 (moderate)

AUC = Area under the curve

INTRODUCING THE PAEDIATRIC NUTRITION RESCREENING TOOL (PNRT)

• Has the child had reduced nutritional intake in the last 7 days?

• Has the child lost weight in the last 7 days?

• A positive answer to either question indicates the need for a full nutrition assessment

MAREE FERGUSONMBA, PHD, RDN, FAND

How do you manage referrals generated from nutrition screening?

How do you audit nutrition screening compliance?

How do you maximise the identification of children who are malnourished to ensure they are recognised by the international disease classification system

(coded)?

Is a single data point adequate to identify and document malnutrition? Could this lead to over diagnosing

malnutrition?

Should you diagnose a child with malnutrition but who is energetic, has good functional status and whose other nutrition-focused

physical findings do not suggest malnutrition?

For example, a child with a BMI for age z-score of -2.15 no signs of micronutrient deficiency, who is energetic,

with good functional status

Why is only severe malnutrition associated with height-for-age

z-score?

Inaccuracy in height/length measurement is a major issue for pediatric dietitians, how does this impact the diagnosis of pediatric

malnutrition?

THANK YOU!